Using Caterpillar to Nibble Small-Scale Images

Recently, MLP-based models have become popular and attained significant performance on medium-scale datasets (e.g., ImageNet-1k). However, their direct applications to small-scale images remain limited. To address this issue, we design a new MLP-based network, namely Caterpillar, by proposing a key module of Shifted-Pillars-Concatenation (SPC) for exploiting the inductive bias of locality. SPC consists of two processes: (1) Pillars-Shift, which is to shift all pillars within an image along different directions to generate copies, and (2) Pillars-Concatenation, which is to capture the local information from discrete shift neighborhoods of the shifted copies. Extensive experiments demonstrate its strong scalability and superior performance on popular small-scale datasets, and the competitive performance on ImageNet-1K to recent state-of-the-art methods

An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization

Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation. However, like other generative models, diffusion models are prone to privacy issues. In this paper, we propose an efficient query-based membership inference attack (MIA), namely Proximal Initialization Attack (PIA), which utilizes groundtruth trajectory obtained by $\epsilon$ initialized in $t=0$ and predicted point to infer memberships. Experimental results indicate that the proposed method can achieve competitive performance with only two queries on both discrete-time and continuous-time diffusion models. Moreover, previous works on the privacy of diffusion models have focused on vision tasks without considering audio tasks. Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task. To the best of our knowledge, this work is the first to study the robustness of diffusion models to MIA in the TTS task. Experimental results indicate that models with mel-spectrogram (image-like) output are vulnerable to MIA, while models with audio output are relatively robust to MIA. {Code is available at \url{https://github.com/kong13661/PIA}}

Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios

Multi-view clustering (MvC) aims at exploring category structures among multi-view data without label supervision. Multiple views provide more information than single views and thus existing MvC methods can achieve satisfactory performance. However, their performance might seriously degenerate when the views are noisy in practical scenarios. In this paper, we first formally investigate the drawback of noisy views and then propose a theoretically grounded deep MvC method (namely MvCAN) to address this issue. Specifically, we propose a novel MvC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore, a non-parametric iterative process is designed to generate a robust learning target for mining multiple views' useful information. Theoretical analysis reveals that MvCAN works by achieving the multi-view consistency, complementarity, and noise robustness. Finally, experiments on extensive public datasets demonstrate that MvCAN outperforms state-of-the-art methods and is robust against the existence of noisy views

Children’s Non-symbolic and Symbolic Numerical Representations and Their Associations With Mathematical Ability

Most empirical evidence supports the view that non-symbolic and symbolic representations are foundations for advanced mathematical ability. However, the detailed development trajectories of these two types of representations in childhood are not very clear, nor are the different effects of non-symbolic and symbolic representations on the development of mathematical ability. We assessed 253 4- to 8-year-old children’s non-symbolic and symbolic numerical representations, mapping skills, and mathematical ability, aiming to investigate the developmental trajectories and associations between these skills. Our results showed non-symbolic numerical representation emerged earlier than the symbolic one. Four-year-olds were capable of non-symbolic comparisons but not symbolic comparisons; five-year-olds performed better at non-symbolic comparisons than symbolic comparisons. This performance difference disappeared at age 6. Children at age 6 or older were able to map between symbolic and non-symbolic quantities. However, as children learn more about the symbolic representation system, their advantage in non-symbolic representation disappeared. Path analyses revealed that a direct effect of children’s symbolic numerical skills on their math performance, and an indirect effect of non-symbolic numerical skills on math performance via symbolic skills. These results suggest that symbolic numerical skills are a predominant factor affecting math performance in early childhood. However, the influences of symbolic and non-symbolic numerical skills on mathematical performance both declines with age

The Mechanism and Pathways of Dopamine and Dopamine Agonists in Prolactinomas

Dopamine agonists such as bromocriptine and cabergoline are the predominant treatment drugs for prolactinoma by inhibiting prolactin secretion and shrinking tumor size. However, the pathways of either dopamine or its agonists that lead to the death of cells are incompletely understood and some are even conflicting conclusions. The main aim of this paper is to review the different pathways of dopamine and its agonists in prolactinomas to help to gain a better understanding of their functions and drug resistance mechanisms

The Smaller the Power Distance, the More Genuine the Emotion: Relationships between Power Distance, Emotional Labor, and Emotional Exhaustion among Chinese Teachers

Using Grandey’s model of emotional labor, this study attempted to reveal the effects of cultural and social factors on teachers’ emotions. Specifically, taking a sample of 3312 Chinese teachers, we examined the effects of power distance (PD) and emotional labor on emotional exhaustion, focusing on the mediating role of emotional labor with different interactive partners. The results showed that Chinese teachers used surface acting (SA) the most with parents, and the least with students; they used the expression of naturally felt emotions (ENFE) the most with students, and the least with colleagues and leaders. They also used deep acting more when working with students and parents. In addition, PD negatively influenced ENFE and positively influenced SA with the three interactive partners. Only SA mediated the relationship between PD and exhaustion. These results improve our understanding of teachers’ emotions in terms of power and suggest that we should consider personal psychological factors (i.e., emotional labor), social factors (i.e., interactive partners), and national culture (i.e., PD) to promote teachers’ well-being

The Smaller the Power Distance, the More Genuine the Emotion: Relationships between Power Distance, Emotional Labor, and Emotional Exhaustion among Chinese Teachers

Using Grandey’s model of emotional labor, this study attempted to reveal the effects of cultural and social factors on teachers’ emotions. Specifically, taking a sample of 3312 Chinese teachers, we examined the effects of power distance (PD) and emotional labor on emotional exhaustion, focusing on the mediating role of emotional labor with different interactive partners. The results showed that Chinese teachers used surface acting (SA) the most with parents, and the least with students; they used the expression of naturally felt emotions (ENFE) the most with students, and the least with colleagues and leaders. They also used deep acting more when working with students and parents. In addition, PD negatively influenced ENFE and positively influenced SA with the three interactive partners. Only SA mediated the relationship between PD and exhaustion. These results improve our understanding of teachers’ emotions in terms of power and suggest that we should consider personal psychological factors (i.e., emotional labor), social factors (i.e., interactive partners), and national culture (i.e., PD) to promote teachers’ well-being

Exploring the Relationships between Pre-Service Preparation and Student Teachers’ Social-Emotional Competence in Teacher Education: Evidence from China

The role of social-emotional competence in sustaining teachers’ professional development has been increasingly gaining prominence. Using Bronfenbrenner’s ecological systems theory, this study attempted to explore the deep internal mechanisms of the influence of university climate on student teachers’ social-emotional competence in the context of China. A cluster sampling method was used to conduct a questionnaire survey on 1776 student teachers from 20 universities in 17 provinces of China. This study uses a structural equation model to analyze the effect of university climate and basic psychological needs on social-emotional competence, which is moderated by relative deprivation. This study found that university climate has a significant positive effect on social-emotional competence; the association between university climate and social-emotional competence is mediated by basic psychological needs; relative deprivation plays a moderating role. The direct effect of university climate on social-emotional competence and the path from university climate to basic psychological needs were moderated by relative deprivation. Specifically, compared with low relative deprivation individuals, the university climate had a weaker positive effect on social-emotional competence and basic psychological needs in high relative deprivation individuals. Based on above empirical evidence, this study shed light on the mechanism for cultivating student teachers’ social-emotional competence, thus improving our understanding of the sustainable professional development of teachers from an emotional perspective

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism